The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 05, 2024

Filed:

Apr. 15, 2022
Applicant:

Fastvdo Llc, Melbourne, FL (US);

Inventors:

Pankaj N. Topiwala, Cocoa Beach, FL (US);

Madhu Peringassery Krishnan, Columbia, MD (US);

Wei Dai, Clarksville, MD (US);

Assignee:

FASTVDO LLC, Melbourne, FL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04N 19/154 (2014.01); G06N 3/08 (2023.01); G06N 20/10 (2019.01); G06T 3/4046 (2024.01); G06T 5/00 (2024.01); G06T 7/00 (2017.01); G06T 7/254 (2017.01); G06T 9/00 (2006.01); H04N 19/107 (2014.01); H04N 19/124 (2014.01); H04N 19/172 (2014.01); H04N 19/174 (2014.01); H04N 19/176 (2014.01); H04N 19/567 (2014.01); H04N 21/234 (2011.01); H04N 21/2343 (2011.01); H04N 21/236 (2011.01);
U.S. Cl.
CPC ...
H04N 19/154 (2014.11); G06N 3/08 (2013.01); G06N 20/10 (2019.01); G06T 3/4046 (2013.01); G06T 5/00 (2013.01); G06T 7/0002 (2013.01); G06T 7/254 (2017.01); G06T 9/002 (2013.01); H04N 19/107 (2014.11); H04N 19/124 (2014.11); H04N 19/172 (2014.11); H04N 19/174 (2014.11); H04N 19/176 (2014.11); H04N 19/567 (2014.11); H04N 21/23418 (2013.01); H04N 21/2343 (2013.01); H04N 21/236 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20224 (2013.01);
Abstract

Video quality analysis may be used in many multimedia transmission and communication applications, such as encoder optimization, stream selection, and/or video reconstruction. An objective VQA metric that accurately reflects the quality of processed video relative to a source unprocessed video may take into account both spatial measures and temporal, motion-based measures when evaluating the processed video. Temporal measures may include differential motion metrics indicating a difference between a frame difference of a plurality of frames of the processed video relative to that of a corresponding plurality of frames of the source video. In addition, neural networks and deep learning techniques can be used to develop additional improved VQA metrics that take into account both spatial and temporal aspects of the processed and unprocessed videos.


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